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Faye Hutsell

On April 8th, San Francisco played host to one of the most consequential gatherings in AI infrastructure this year. Our Beyond Summit, formerly known as Beyond CUDA, convened over 300+ engineers, researchers, founders, and infrastructure leaders at the forefront of reshaping how AI systems are built and scaled. Backed by AMD as the Platinum Sponsor, the summit represented a watershed moment: the coming-of-age of open, heterogeneous AI infrastructure.

Beyond Summit 2026: Top Learnings from the Frontlines of Open AI Infrastructure

A Deep Dive into the Future of Heterogeneous Compute, Open Ecosystems, and the Post-Monoculture Era

On April 8th, San Francisco played host to one of the most consequential gatherings in AI infrastructure this year. Our Beyond Summit, formerly known as Beyond CUDA, convened over 300+ engineers, researchers, founders, and infrastructure leaders at the forefront of reshaping how AI systems are built and scaled. Backed by AMD as the Platinum Sponsor, the summit represented a watershed moment: the coming-of-age of open, heterogeneous AI infrastructure.

The event's evolution from "Beyond CUDA" to "Beyond Summit" was itself a statement. Production AI will not consolidate around a single architecture, a single vendor, or a single cloud. The future is heterogeneous, open, and built by a rapidly growing community of practitioners who are proving that world-class AI doesn't require submitting to monopolistic pricing or playing allocation games with scarce capacity.

What emerged from the keynotes, technical deep-dives, and hallway conversations was a coherent vision for the future of AI infrastructure, one built on open foundations, practical portability, and a relentless focus on real production workloads rather than benchmark heroics. Here are our top learnings from Beyond Summit 2026.


1. The CUDA Monoculture is Cracking Faster Than Expected

The most resounding message from Beyond Summit was delivered not through any single presentation but through the collective presence of the event itself. The ecosystem building around AMD Instinct GPUs, ROCm, and open infrastructure has reached a critical inflection point. Industry analysts and as of late SemiAnalysis, one of Beyond’s featured presenters have long argued that AMD is the most credible challenger to Nvidia's AI dominance. The evidence presented throughout the day from production deployment case studies to performance benchmarks suggested that challenger status has evolved into something more substantial: a viable, production-ready alternative.

What's changed isn't just hardware capability, although AMD's MI300 series and particularly the MI355X have closed significant gaps. It's the maturation of the entire software stack. ROCm has evolved from a promising but rough-around-the-edges platform to a robust ecosystem capable of supporting serious production workloads. The summit featured ROCm optimization for training and inference, with practitioners sharing concrete techniques for achieving steady performance under real load.

Jeff Tatarchuk, Co-founder and our Chief Growth Officer at TensorWave offered clear framing: we are entering a new phase where choice is no longer theoretical. Teams are deploying AMD GPUs in production, running large-scale training and inference workloads, and achieving cost efficiencies beyond what a single-vendor ecosystem allows. CUDA was always meant to be surpassed and now may be the time. To truly sustain AI, we must optimize every layer, kernel, hardware, compiler, chiplet, orchestration, serving, and application. We must build an ecosystem of equal gravity, one that prioritizes optionality over lock-in, and foster open, sustainable progress, where every layer evolves together.


2. Heterogeneous Systems Are No Longer Theoretical but a Necessity

Throughout the summit, speakers returned to a central theme: heterogeneous compute isn't a future aspiration but a present reality. As models grow larger and more complex, and as inference demands scale exponentially, the idea that any single vendor or architecture can meet all requirements has become increasingly untenable.

Greg Diamos, Co-founder of ScalarLM and a pioneering figure in AI systems emphasized that the teams succeeding at scale are those building architectural flexibility into their infrastructure from the start. This isn't about hedging bets but about recognizing that different workloads have different optimal configurations, and that supply chain resilience requires alternative sources of compute capacity.

The summit featured technical content on multi-GPU training patterns, distributed systems optimization, and practical strategies for maintaining portability across frameworks like PyTorch, JAX, and vLLM. The message was clear: the teams thriving in the current environment are those who've invested in building systems that can evolve as quickly as the technology itself.


3. ROCm Has Reached Production Maturity

A key topic at Beyond Summit was ROCm, AMD's open-source software platform for GPU computing. Anush Elangovan, VP of AI Software at AMD, and Neha Prakriya, who leads Pre-training at Scale at AMD, presented technical sessions that highlighted optimization pathways that would have been unthinkable just two years ago.

The platform has matured dramatically, with significant improvements in kernel support, memory management, and integration with popular frameworks. Practitioners shared real-world performance data showing ROCm achieving competitive and in some cases superior performance on specific workloads compared to CUDA. Many sessions covered ROCm tips for training and inference, MI355X performance tuning, and kernel development for attention, quantization, and custom operations.

What's particularly notable is the open nature of ROCm itself. Unlike proprietary alternatives, ROCm allows teams to inspect, modify, and contribute to the underlying platform. This transparency has fostered a growing community of developers who are collectively advancing the ecosystem. The summit featured maintainers from multiple open-source projects who are building on ROCm, demonstrating that the platform's openness is a genuine competitive advantage.

Neha Prakriya from AMD's Training and Inference Optimization Team presented an agentic workload optimization system that automates GPU performance tuning for AI workloads. Her demonstration showed AMD MI300X achieving 2.83x throughput gains on GLM-4 training (174→988 tokens/sec/GPU in 8 hours) and matching competitor inference performance with +47-86% improvements, all without manual kernel engineering. For builders, this means you can extract maximum hardware performance from AMD GPUs through an intelligent, closed-loop optimization system that continuously discovers and applies improvements, eliminating the need for specialized optimization teams and accelerating time-to-production for AI models.

Anush Elangovan's "AMD and the Developer Experience" presentation at Beyond Summit frames the AI era as a fundamental reshaping of what makes software engineers valuable. He introduces the "RISES vs FALLS" framework: the skills that RISES which mean System thinking, Judgment & taste, Problem framing, and Stakeholder alignment as the strategic capabilities AI cannot replicate, vs. what FALLS, which includes Rote implementation, Syntax memorization, Boilerplate production, and Isolated coding speed all of which are being automated away. For builders, this matters because ROCm and AMD's ecosystem represent more than just an alternative to NVIDIA, they embody a shift where developers must think across platforms and architectures rather than optimizing for a single vendor's lock-in. The message: those who master system-level thinking and cross-platform fluency will thrive in the AI-accelerated future, while those competing with AI on mechanical coding tasks will not.


4. Independent Benchmarking Is Essential in the AI Hardware Battle

The "State of Benchmarking" panel at Beyond Summit brought together three influential voices in AI performance measurement: David Kanter, founder and executive director of MLCommons (MLPerf), Dylan Patel, founder and chief analyst at SemiAnalysis, and Micah Hill-Smith, co-founder of Artificial Analysis.

Dylan Patel delivered the panel's most memorable line: "Vendors Lie." His point cuts to the heart of the benchmarking crisis, hardware manufacturers have every incentive to game benchmarks, optimize for specific tests, and present numbers that don't reflect real-world workloads. For builders making infrastructure decisions, uncritically accepting vendor claims can lead to costly mistakes.

David Kanter brought the counterweight: MLPerf's rigorous, peer-reviewed benchmarks have recorded over 56,000 results, documenting performance gains of up to 50x over five years, significantly outpacing Moore's Law. His key insight: "What gets measured, gets improved." Standardized benchmarks drive industry progress by creating apples-to-apples comparisons that vendors can't easily manipulate.

Micah Hill-Smith rounded out the discussion with a critical operational point: labs prompt models differently, cherry-pick examples, and self-report inflated numbers. Artificial Analysis addresses this through their "mystery shopper policy" in that they register anonymous accounts and run benchmarks incognito to prevent labs from serving different models on private endpoints. Hill-Smith's team has become the trusted independent source for real-world performance data across models, providers, and hardware.

For builders, the message was clear: in an era where vendors optimize for benchmarks and labs hype their numbers, independent verification isn't optional but essential for making informed infrastructure decisions.


5.  Running CUDA Code on AMD without rewrites is possible

Michael Søndergaard, CEO of Spectral Compute, presented SCALE at Beyond, a GPU programming toolkit and compiler that enables CUDA-based code to run natively on AMD GPUs (and eventually other AI accelerators) without the costly rewrites that existing porting tools require. For builders, this matters profoundly: it eliminates the "Nvidia tax", the premium costs, 18-month wait times, and vendor lock-in that currently force developers to use Nvidia hardware exclusively. SCALE allows teams to deploy their existing CUDA code on alternative GPUs in seconds rather than months, enabling true multivendor infrastructure strategies for the first time. As Søndergaard frames it, developers can finally "write CUDA once and run it anywhere," unlocking the freedom to choose hardware based on performance, efficiency, and availability rather than ecosystem lock-in.


6. Real TCO Numbers Favor Open Infrastructure When Properly Optimized

Waleed Atallah, founder and CEO of Makora, brings hard-won insights from years of wrestling with the tedious, hardware-specific grind of AI model optimization. His company builds software that automates GPU kernel optimization across diverse hardware platforms, cutting AI compute costs by up to 70% without requiring teams of specialized engineers.

At Beyond Summit, Waleed tackled the exploding complexity of GPU infrastructure  where the proliferation of vendors (NVIDIA, AMD, Intel) has created a combinatorial explosion of possible configurations. Rather than chasing the next hardware upgrade, he introduced Sequential Monte Carlo Speculative Decoding as a software-level path to dramatic performance gains. This technique accelerates LLM inference by predicting and verifying multiple tokens simultaneously, collapsing sequential wait times into faster, multi-token chunks without changing hardware. The takeaway for builders is clear: in an era of hardware fragmentation, algorithm optimization is the lever that scales across vendor ecosystems and delivers compounding returns.


7. AI-Generated Kernels Will Democratize Inference

Emilio Andere, co-founder of Wafer, argued that NVIDIA's dominance stems not from hardware superiority but from a software moat built over 20 years of CUDA investment and one that AI-generated kernels are poised to dismantle. While competing chips from AMD and others now match NVIDIA in raw flops, the real performance gap lies in kernel optimization: a senior GPU engineer costs ~$300k/year, but frontier coding models can produce equivalent work at ~0.1% of that cost. The critical insight Emilio delivered is that the bottleneck isn't model intelligence, it's verification environments. LLMs are already capable of writing performant kernels, but they tend to cheat when environments lack proper constraints. So if you can measure performance perfectly and your agent searches long enough, you can converge to state of the art. The environment is the bottleneck, not the model.

For inference builders, the implications are profound. If AI can automatically generate optimized kernels for any hardware platform, the vendor lock-in that keeps teams tethered to NVIDIA dissolves. Builders gain the freedom to choose the most cost-effective hardware for their inference workloads without requiring rare CUDA expertise or months of optimization time. Emilio's core message: "Hardware companies with software moats are not safe"  because once you have adequate testing environments, the job of porting performant kernels to competitor chips gets dramatically easier. The future of inference is hardware-agnostic, and AI is the bridge.


8. Automated Architecture Discovery Is the Future of Edge AI

Mathias Lechner, CTO and co-founder of Liquid AI presented a compelling approach to designing foundation models specifically optimized for edge deployment. His key insight is that while most AI development focuses on data center optimization, the vast majority of silicon actually lives outside data centers in phones, laptops, cars, and factories, each with vastly different memory, bandwidth, and latency constraints. Rather than manually designing architectures, Mathias introduced an automated foundation model design system using evolutionary strategies that parameterize the design space and optimize for three critical metrics: evaluation performance, on-device inference speed, and memory footprint.

 This approach discovered LFM2, a hybrid architecture combining group query attention transformers with double gated short convolution layers, which delivers significantly faster prefill and decode speeds while maintaining competitive quality. The results speak for themselves: LFM2 has achieved 80 million downloads since its July release, with 1 million additional downloads weekly, demonstrating that inference-aware architectures optimized for actual hardware constraints, and not just theoretical performance are what the market truly needs. Mathias emphasized that they're moving beyond Chinchilla optimal training (30T tokens) to inference optimal, and while their current design space is constrained by available kernels, future iterations will explore much larger architectural spaces to unlock even greater efficiency gains.


The Path Forward

Beyond Summit 2026 represented more than a technical conference; it was a statement of intent from a community that's building the infrastructure for AI's next chapter. The rebranding from "Beyond CUDA" to "Beyond Summit" reflected a broader truth: this movement isn't defined by opposition to any single vendor but by a positive vision for what AI infrastructure can become.

That vision is simply open, heterogeneous, production-focused, and community-driven. The practitioners gathered in San Francisco on April 8th aren't just talking about alternatives; they're building them, deploying them, and proving them in production environments that matter.

The teams building on open, high-performance infrastructure are proving that world-class AI doesn't require submitting to monopolistic pricing or playing allocation games with scarce capacity. They're demonstrating that a second engine, high-performance, open infrastructure isn't just an alternative. It's a production necessity.

Everyone agrees that today’s AI infrastructure stack is too rigid, too hardware-dependent, and too difficult to optimize at scale. Where the field diverges is in how to fix it. Makora and Wafer are betting that AI itself should build the infrastructure, using LLM agents to generate and optimize GPU kernels end-to-end across hardware targets. Modular is taking a more foundational route, arguing that the answer is a new language altogether: Mojo, designed to eliminate the “two-language problem” by being fast enough for both research and production while compiling natively across NVIDIA, AMD, and Apple Silicon. Lemurian Labs pushes the idea even further, arguing that kernels are the wrong abstraction entirely, that compute is not the true bottleneck, data movement is, and that the real breakthrough must come from compilers that treat the system as a unified compute fabric. Spectral Compute and ZLUDA take the most pragmatic path: don’t force developers to rewrite anything, just recompile existing CUDA workloads so they can run across vendors. The consensus is clear on what is broken; the race is now about which radically different path will define what comes next.

The conversations at Beyond Summit cut through the noise to address what actually matters: real performance numbers, real cost models, and real deployment patterns. For anyone building AI systems at scale, the message was clear: the future of AI infrastructure is being built right now, and it's more open, more diverse, and more practical than the monoculture narrative would suggest. A final note of thanks to the remarkable sponsors for making this possible:


TensorWave Cloud: Engineered for AI Velocity

TensorWave is the industry’s premier AMD-exclusive cloud provider, delivering memory optimized AI infrastructure for training, fine-tuning and inference. 

Our platform combines the raw computational power of AMD's latest GPU architectures with innovative liquid cooling technology and ultra-high-speed networking, creating an optimized environment for the most demanding AI workloads. From large language model training to real-time inference, TensorWave provides the performance, scalability, and cost-efficiency that modern AI initiatives demand.

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Beyond Summit 2026: Top Learnings from the Frontlines of Open AI Infrastructure

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